API KEY
WEB SEARCH
Every web search, URL extraction, and research query your agent needs to run is reachable via Tavily. Tavily MCP gives your agent authenticated access to AI-optimized web search scoped to the user who authorized it.
import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";
const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["tavilymcp"], toolNames: ["tavily_search", "tavily_search_context", "tavily_extract"] },
pageSize: 100,
});
const lcTools = tools.map((t) => new DynamicStructuredTool({
name: t.tool.definition.name,
description: t.tool.definition.description,
schema: z.object({}).passthrough(),
func: async (args) => {
const { data } = await sk.tools.executeTool({
toolName: t.tool.definition.name,
identifier: "user_123",
params: args,
});
return JSON.stringify(data);
},
}));
const agent = createReactAgent({ llm, tools: lcTools });import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";
const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();
const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["tavilymcp"], toolNames: ["tavily_search", "tavily_search_context", "tavily_extract"] },
pageSize: 100,
});
const llmTools = tools.map((t) => ({
type: "function",
function: {
name: t.tool.definition.name,
description: t.tool.definition.description,
parameters: t.tool.definition.input_schema,
},
}));
const resp = await openai.responses.create({
model: "gpt-4o", input: prompt, tools: llmTools,
});import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";
const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();
const { tools } = await sk.tools.listScopedTools("user_123", {
filter: { connectionNames: ["tavilymcp"], toolNames: ["tavily_search", "tavily_search_context", "tavily_extract"] },
pageSize: 100,
});
const llmTools = tools.map((t) => ({
name: t.tool.definition.name,
description: t.tool.definition.description,
input_schema: t.tool.definition.input_schema,
}));
const msg = await anthropic.messages.create({
model: "claude-sonnet-4-6", max_tokens: 1024,
tools: llmTools,
messages: [{ role: "user", content: prompt }],
});import { Agent } from "@google/adk/agents";
import {
MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";
const toolset = new MCPToolset({
connectionParams: new StreamableHTTPConnectionParams({
url: "https://mcp.scalekit.com/tavilymcp",
headers: { Authorization: `Bearer ${userScopedToken}` },
}),
});
const agent = new Agent({
name: "agent", model: "gemini-2.0-flash",
tools: await toolset.getTools(),
});
.svg)
// shared token
audit → bot_service_account
user_filter → broken
// scalekit
audit → user_abc
scope → enforced ✓
.svg)
